The Glasgow Admission Prediction Score

Allan Cameron

Consultant Physician, Glasgow Royal Infirmary

Outline

• The need for an admission prediction score

• What is GAPS?

• GAPS versus human judgment and Amb Score

• GAPS as a predictor of adverse outcomes

• The role of GAPS in ambulatory care

Mortality and admission odds against length of ED stay

Advantages of predicting admission

• Identifying as early as possible which patients are

likely to be admitted and which are likely to be

discharged could promote efficiency:

• Identifying patients for ambulatory care

• Bed management

• Decision support

• Patient streaming

• Triage is first clinical assessment made in ED

• Triage staff cannot accurately predict admission

Background

• Several tools have been created to predict admission

at the point of triage

• The simpler tools lack accuracy

• The accurate tools lack simplicity

• We have lacked a simple but accurate tool to assess

the probability of admission at the time of triage

Glasgow admission prediction score

Variable Points

Age 1 point per decade

NEWS score 1 point per point on NEWS score

Triage category: 3 5

2 10

1 20

Referred by GP 10

Arrived in ambulance 5

Admission within 1 year 5

Methods

• Multi-centre, retrospective, cross-sectional study

• 322,846 unscheduled secondary care attendances in

North Glasgow over a two-year period

• Two-thirds of attendances were selected at random

to create the prediction score using variables already

available at triage

• Score created from mixed-effects multiple logistic

regression model

• The score was then tested for accuracy on the

remaining third by assessing its ROC curve

Results

• 344,429 adult attendances over 2 years

• After discounting transfers between units and

missing data, 322,846 attendances were available for

analysis in 191,653 patients

• 123,397 of the 322,846 attendances led to admission

(38.22%)

• 215,231 attendances used to create the score

• 107,615 attendances used to test the score

Criticism

• Do we really need another score? Whatever happened to

clinical judgement?

GAPS versus human judgment

• Comparison of accuracy of triage nurses and GAPS

• Prospective study of 1,838 ED attendances

• Of these, 766 (41.7%) were admitted

• Triage staff asked to estimate probability of admission (VAS)

• Nurses were only accurate in predicting admission when they

were very confident of the outcome (92.4%) but accuracy was

poor in the majority of cases (68.8% accurate)

• When the nurses were less confident, GAPS was significantly

more accurate and better calibrated

Criticism

• This score predicts admissions. How can we use it to facilitate

ambulatory care? Don’t we already have a score for that?

GAPS versus Amb Score

Prospective study, GRI-led multi-site collaboration

Consecutive patients presenting for ED triage

Researchers worked in shifts to cover all 168 hours of

the week

Each patient interviewed to calculate GAPS and Amb

Scores

Patients followed up to 30 days

Endpoint was admission to hospital or ED discharge

Comparison of AUC of ROC using DeLong’s method

Results

• 1496 adults attending ED triage during study

• Of these, 64 IRDs, leaving 1432 for analysis

• 570 (39.8%) admitted

• AUC 0.808 for GAPS, compared to 0.743 for Ambs,

p<0.00001

• GAPS had net classification improvement of 6% over

Amb

Criticism

• Surely this just tells you whether someone will be admitted,

not whether they should be admitted?

• We followed up all 1432 patients to six months

• Split patients into equal 3 tertiles based on GAPS

• Survival analysis using all-cause mortality

• “Survival” analysis of hospital readmission

• “Survival” analysis of hospital length of stay on the 563

patients admitted to hospital (7 lost to follow-up)

Prospective follow-up

p < 0.0001

p=0.026

Implementation

• We have been using GAPS at our Acute Assessment Unit in

Glasgow Royal Infirmary for over two years

• Of 1600 monthly GP referred medical attendances, around

30% can be sent directly to our ambulatory unit using the

single criterion of low GAPS (<25)

• Achieves a high discharge rate from ambulatory first

assessment of ~90% with excellent safety record

• Allows ambulatory care to be patient-based rather than

condition-based

• GAPS has now been taken up by several UK sites

Conclusions

• We have derived a simple but accurate way to assess

probability of admission at triage

• It predicts death, reattendance and readmission

within 28 days

• It usually outperforms experienced triage staff

• It outperforms the current method recommended by

the RCP toolkit for streaming to ambulatory care

• It can be used to measure (or control for) patient

factors when looking at admission rates

Further challenges

• How can we better use the information GAPS gives

us in real time?

• How can we use the information GAPS gives us for

service planning?

• Dissemination and implementation.

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